Unmanned bicycle balancing via Lyapunov rule-based fuzzy control

Hashemnia, Saeed; Panahi, Masoud Shariat; Mahjoob, M. J.

doi:10.1007/s11044-013-9357-8

Cited by 16 publications

(11 citation statements)

References 22 publications

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“…Hashemnia et al 61 designed fuzzy system rules using Lyapunov stability, and a genetic algorithm (GA) was used to optimize the constant control signal parameters and fuzzy system parameters such as the scaling factors of membership functions. This fuzzy controller could not maintain the entire system’s stability when the controller was applied to control an unmanned bicycle.…”

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Underactuated systems represent a class of mechanical systems with fewer independent control actuators than degrees of freedom. The control problem of underactuated systems is one of the most challenging in the theory of nonlinear control because such systems exhibit strong coupling and highly nonlinear characteristics. Fuzzy systems have been successfully applied to control underactuated systems in both theoretical analysis and practical application. This survey describes the model and application of underactuated systems and classifies fuzzy systems in a straightforward manner. Moreover, this study analyzes problems facing conventional control methods and presents the development of fuzzy systems to solve these problems.

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Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Fuzzy control Model-based fuzzy control [154][155][156][157][158][159][234][235][236] Model-free fuzzy control [160][161][162]164,[166][167][168][169][170][237][238][239] Hybrid fuzzy control [171][172][173][174] The integration of fuzzy control with other traditional control Fuzzy PID composite control [175][176][177][178][179][180][181][182][240][241][242] Sliding mode variable structure control 205, Backstepping control 186,[276][277][278][279] Adaptive fuzzy control 127,189,190,[280][281][282]…”

Section: Related Studies Primary Classification Secondary Classificationmentioning

confidence: 99%

Underactuated robotics: A review

Wang

Liu

2019

International Journal of Advanced Robotic Systems

146

102

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Underactuated robotics is an emerging research direction in the field of robotics. The control input of the underactuated robot is less than the degree of freedom of the system. It has the advantages of lightweight, low energy consumption, excellent performance, and broad development prospects. This article reviews the state of the art on underactuated robotics. On the basis of previous studies, this article takes the non-holonomic constraint equation as the entry point to classify and summarize underactuated robot and their common mechanisms. The controllability of underactuated robot is further discussed. The control flow of underactuated robot is described based on the open-closed control method. In the closed-loop control, the control method based on the fuzzy system is mainly used. Finally, the difficulties in the current research of underactuated robot are summarized, and the future research directions are prospected.

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“…Dao et al [58] further developed a sliding mode controller to stabilize the lateral motions of a Whipple type bicycle model at various forward speeds. A fuzzy controller, but now based on Lyapunov rules, to balance an unmanned bicycle was presented by Hashemnia et al [92]. They demonstrated the robustness of the controller by applying the method to various different bicycle designs.…”

Section: Controlled Dynamicsmentioning

confidence: 99%

State-of-the-art and challenges of railway and road vehicle dynamics with multibody dynamics approaches

et al. 2020

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A review of the current use of multibody dynamics methods in the analysis of the dynamics of vehicles is given. Railway vehicle dynamics as well as road vehicle dynamics are considered, where for the latter the dynamics of cars and trucks and the dynamics of single-track vehicles, in particular motorcycles and bicycles, are reviewed. Commonalities and differences are shown, and open questions and challenges are given as directions for further research in this field.

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Unmanned bicycle balancing via Lyapunov rule-based fuzzy control

Cited by 16 publications

References 22 publications

A survey on the application of fuzzy systems for underactuated systems

A survey on the application of fuzzy systems for underactuated systems

Underactuated robotics: A review

State-of-the-art and challenges of railway and road vehicle dynamics with multibody dynamics approaches

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